Statistics 2026 A-Z™: For Data Science with Both Python & R

Explore how statistics use population and sample to infer data, compare mean estimates, and apply probability and non-probability sampling, including simple random, systematic, stratified, and cluster methods.

Explore descriptive statistics and measures of central tendency, including mean, median, and mode, and learn how outliers affect the mean and why median is more robust in biostatistics.

Explore how measures of variability, including variance, standard deviation, and interquartile range, reveal data spread through quartiles and box plots, and identify outliers.

Explore distributions from normal to skewed, learn central tendency measures, kurtosis, and correlation concepts, and apply them to data science with practical examples.

Practice descriptive statistics with hands-on questions, calculating mean, median, mode, min, max, range, standard deviation, and variance by hand and in Python, including histograms, frequency, and class-interval analyses.

Explore inferential statistics that generalize from samples to populations. Learn point and interval estimation, hypothesis testing, and the limits of drawing causal conclusions from data.

Explore qualitative and quantitative variable types, including discrete and continuous values, plus nominal, ordinal, interval, and ratio scales, with key concepts like sample size, x-bar, mu, and standard deviations.

Understand the central limit theorem: sample means form a near-normal distribution, with mean near the population mean and standard error equal to the population standard deviation divided by sqrt(n).

Master z-score concepts, including the standard score, mean and standard deviation, and using the z-table to find probabilities in a normal distribution; standardize data with (x−μ)/σ.

Learn how confidence intervals quantify the probability that a population parameter lies between two values, with symmetry about the mean and margins of error that depend on sample size.

derive 95% confidence intervals for the mean from the sample mean, s, and n; margin of error is z s over sqrt(n); higher level widens, larger n tightens.

Hypothesis testing introduces null and alternate hypotheses, significance levels, and test statistics, using samples to infer population parameters and prepare to learn about z-test and t-test.

Explore how z and t tests use p values to determine if sample means differ significantly from a population mean, guided by alpha and critical regions.

Explore hypothesis testing by distinguishing type I and type II errors, and learn how p-values and alpha define statistical significance for rejecting or accepting the null hypothesis.

Learn the one-sample, independent, and paired t tests, when to use t tests over z tests, their assumptions, and how to interpret t values with the t table.

Learn chi-square tests of goodness of fit and independence using data to compare observed and expected counts, with p-values and examples like m&m colors, dice outcomes, and zodiac signs.

Learn the chi square test for independence, assessing whether two categorical variables relate, with steps for hypothesis, degrees of freedom, expected counts, and interpretation using gender and voting preferences.

ANOVA tests multiple populations at once, using the F statistic to determine if any mean differs, and explains null and alternative hypotheses and related sum of squared error and treatment.

Explore how to interpret correlation coefficients and select the right inferential tests for categorical and continuous data, using examples of income, age, exercise, and health.

Explore graphpad's calculators for categorical and continuous data, performing chi-square tests, t tests, and ANOVA post test, with descriptive statistics, outlier detection, and p value calculations and a confidence interval.

Learn to apply z-score methods in Python to solve inferential and descriptive statistics problems, using mean and standard deviation, CDF and PPF to find areas and thresholds in normal distributions.

Explore t-tests in data science by applying one-sample, independent, and paired t-tests, along with confidence intervals and the central limit theorem, using Python examples.

Apply chi square goodness-of-fit and chi square tests of independence to compare observed and expected counts and interpret p-values; also cover one-way ANOVA and covariance and correlation.

Explore a house prices dataset with numpy, pandas, and matplotlib; inspect shape and columns, describe statistics, and compare neighborhoods using z-tests, t-tests, chi-square, and anova.

Explores the city payroll dataset case study, cleaning dollar-formatted figures, describing numeric columns, and applying t-tests and one-way ANOVA to compare salaries across years and job titles.